1. Field of the Invention
The invention relates to control systems used in process control environments, particularly to control systems developing optimal target outputs and taking optimal paths from current to desired target conditions for non-linear processes, and more particularly to control systems utilizing neural networks in the optimization processes.
2. Description of the Related Art
In many industrial environments there are numerous processes which must be controlled. Examples include oil refineries, chemical plants, power plants, and so on. For example, in oil refineries base crude oil feed stocks enter the refineries and after numerous individual distillation or reaction processes, a panoply of hydrocarbon streams are output. Each one of the individual processes must be basically controlled or disaster could result. Above this basic standard, each individual process must be properly controlled to develop acceptable outputs.
Each of the processes has numerous inputs or disturbance variables, such as input compositions, feed rates and feed stock temperatures. Numerous outputs or controlled variables also usually are present, such as the properties of various output streams. To perform process control, certain settable items or manipulated variables are also present, such as heating, cooling and recirculation rates.
Conventionally, control of the process was performed using feedback techniques. The controlled variables or outputs were measured and compared against desired values. If errors were present, changes were made to the manipulated variables based on the errors. One problem generally resulting from feedback control was stability concerns in the process, as the time delay may be critical in physical systems. Alternatively, the output may have reflected an undesirable trend or condition too late for adequate control of the process. Historically, proportional-integral-derivative (PID) controllers were utilized for feedback control. One problem generally resulting from the use of PID controllers was lack of multivariable control, as a PID controller only has one input variable and one output variable. Further, no economic control can be readily performed. Most real world systems have numerous controlled, disturbance and manipulated variables. Very complex arrangements of PID controllers could be utilized, but the sheer complexity often limited confidence and testability, much less a determination of optimal control.
Certain feedforward techniques were utilized with some success. These techniques utilized a linear model of the process being controlled and various changes to the manipulated variables were simulated. Preferably the simulation allowed a quicker response time than feedback control and the model preferably utilized all the major disturbance, manipulated and controlled variables of the process, allowing multiple variable control. By using the disturbance or input variables, the controlled variables could be obtained, those generally being present downstream of where the disturbance variables were measured, thus leading to the feedforward definition. However, the existing feedforward, modeling techniques have a major limitation. They operate primarily on linear or linearized systems, where a linear model can be developed. The problem is that most of the more complex processes are by nature non-linear. Further, the models are not updated when minor process changes occur. Thus accuracy and/or range are sacrificed when linear modeling is utilized.
Therefore a control system which can perform fast feedforward control for non-linear systems with high accuracy over a wide range is desirable.